A practical guide to understanding and minimizing context friction in AI-assisted development
Understanding the gap between AI's promise and reality in software development.
Sarah vs Miguel: How two developers approach the same bug with different tools reveals the hidden cost of context friction.
Understanding the four layers of developer context and why they're so easy to lose.
T_total = T_build + T_ai + T_recover. Understanding where time really goes in AI-assisted coding.
A practical worksheet to measure the hidden costs in your own AI coding workflows.
Four components of context-aware AI: Collectors, Synthesizer, Model, and Interaction Layer.
Five design principles that distinguish context-aware AI from chat-first tools.
Four metrics that actually reflect flow and context friction: TTCAA, Flow Session Length, Reorientation Time, and Context Provision Ratio.
Six failure modes: Alt-Tab Copilot, Prompt Theater, Chat-First, Log Dumping, Context Amnesia, and Diff Blindness.
Getting buy-in from leadership, overcoming developer resistance, running pilots, and calculating ROI.
Technical architecture, context gathering implementation, prompt synthesis, and IDE integration with code examples.
What's coming: multi-file refactoring, proactive suggestions, project-wide context, and runtime integration.
Actionable scorecards, pilot checklists, and concrete next steps for evaluators, engineers, and builders.
This book is free to read online. Navigate through chapters using the table of contents above.
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